REPOGEO REPORT · LITE

Osilly/Vision-R1

Default branch main · commit e33b95d6 · scanned 6/25/2026, 12:23:49 PM

GitHub: 1,469 stars · 27 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

30 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Osilly/Vision-R1, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening paragraph to explicitly state the project's core identity

Why:

CURRENT

The official repo for "Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models".

COPY-PASTE FIX

Vision-R1 is a novel Multimodal Large Language Model (MLLM) that leverages R1-like Reinforcement Learning (RL) and cold-start initialization to significantly enhance reasoning capabilities. This repository provides the official implementation, models, and datasets for our ICLR 2026 paper, 'Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models'.

hightopics#2

Add relevant topics to the repository

Why:

COPY-PASTE FIX

multimodal-llm, mllm, large-language-models, llm-reasoning, reinforcement-learning, rl, computer-vision, iclr2026, deep-learning, ai-research

highlicense#3

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a `LICENSE` file in the root of the repository with a suitable open-source license (e.g., MIT License or Apache-2.0) that aligns with your project's goals.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface Osilly/Vision-R1

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
PyTorch · recommended 2×
TensorFlow · recommended 2×
TRL (Transformer Reinforcement Learning) · recommended 1×
DeepMind's Acme · recommended 1×

CATEGORY QUERY
How to enhance reasoning abilities in multimodal large language models using reinforcement learning?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. TRL (Transformer Reinforcement Learning)
3. DeepMind's Acme
4. OpenAI's Triton
5. LangChain
6. LlamaIndex
7. PyTorch
8. TensorFlow
9. Gymnasium
10. Stable Baselines3
11. RLlib (Ray RLlib)
AI recommended 11 alternatives but never named Osilly/Vision-R1. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks exist for developing multimodal LLMs with advanced mathematical and visual reasoning?
you: not recommended
AI recommended (in order):
1. PyTorch
2. Hugging Face Transformers
3. Hugging Face Diffusers
4. TensorFlow
5. Keras
6. TensorFlow Hub
7. JAX
8. Flax
9. Haiku
10. OpenAI API
11. GPT-4V
12. DALL-E 3
13. Microsoft DeepSpeed
14. PyTorch Lightning
AI recommended 14 alternatives but never named Osilly/Vision-R1. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of Osilly/Vision-R1?
pass
AI named Osilly/Vision-R1 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Osilly/Vision-R1 in production, what risks or prerequisites should they evaluate first?
pass
AI named Osilly/Vision-R1 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo Osilly/Vision-R1 solve, and who is the primary audience?
pass
AI named Osilly/Vision-R1 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/Osilly/Vision-R1.svg)](https://repogeo.com/en/r/Osilly/Vision-R1)

HTML

<a href="https://repogeo.com/en/r/Osilly/Vision-R1"><img src="https://repogeo.com/badge/Osilly/Vision-R1.svg" alt="RepoGEO" /></a>

Pro

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Osilly/Vision-R1 — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite